import geopandas as pd
import contextily as ctx # Used for contextual basemaps
import matplotlib.pyplot as plt
from geocube.api.core import make_geocube # Used for rasterizing
import os
import shapely
import imageio # Used for making animated GIFs
import numpy as np
from IPython.display import Image
from osgeo import gdal # Raster operations
plt.rcParams['figure.figsize'] = (20, 20)
os.listdir("input")
/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow. warnings.warn(
['lds-nz-road-centrelines-topo-150k-FGDB.zip', 'lds-nz-8m-digital-elevation-model-2012-GTiff-auckland-region.zip', 'statsnzpopulation-by-meshblock-2013-census-FGDB.zip', 'statsnz2018-census-electoral-population-meshblock-2020-FGDB.zip', 'statsnzregional-council-2021-clipped-generalised-FGDB.zip', 'lris-lcdb-v50-land-cover-database-version-50-mainland-new-zealand-FGDB.zip']
First, read regional council bounds. This geometry will be used to clip NZ-wide datasets to just the region of interest, Auckland
%%time
REGC = pd.read_file("input/statsnzregional-council-2021-clipped-generalised-FGDB.zip!regional-council-2021-clipped-generalised.gdb")
AKL = REGC[REGC.REGC2021_V1_00_NAME == "Auckland Region"].copy()
# Filter out islands
AKL["geometry"] = max(AKL.geometry.explode(), key=lambda a: a.area)
# Coordinate reference system (projection)
print(AKL.crs)
# Simplify geometry to speed up clip operations
AKL = AKL.simplify(1000).buffer(1000)
ax = AKL.to_crs(epsg=3857).boundary.plot()
ax.set_title("Auckland Region clip extent")
ctx.add_basemap(ax)
epsg:2193 CPU times: user 1.78 s, sys: 935 ms, total: 2.71 s Wall time: 14.4 s
Load the LRIS Land Cover Database (downloaded in GDB format from https://lris.scinfo.org.nz/layer/104400-lcdb-v50-land-cover-database-version-50-mainland-new-zealand/)
%%time
df = pd.read_file("zip://input/lris-lcdb-v50-land-cover-database-version-50-mainland-new-zealand-FGDB.zip!lcdb-v50-land-cover-database-version-50-mainland-new-zealand.gdb")
CPU times: user 1min 47s, sys: 2.5 s, total: 1min 49s Wall time: 1min 49s
print(df.columns)
print(df.crs)
display(df.sample(5))
Index(['Name_2018', 'Name_2012', 'Name_2008', 'Name_2001', 'Name_1996',
'Class_2018', 'Class_2012', 'Class_2008', 'Class_2001', 'Class_1996',
'Wetland_18', 'Wetland_12', 'Wetland_08', 'Wetland_01', 'Wetland_96',
'Onshore_18', 'Onshore_12', 'Onshore_08', 'Onshore_01', 'Onshore_96',
'EditAuthor', 'EditDate', 'LCDB_UID', 'geometry'],
dtype='object')
epsg:2193
| Name_2018 | Name_2012 | Name_2008 | Name_2001 | Name_1996 | Class_2018 | Class_2012 | Class_2008 | Class_2001 | Class_1996 | ... | Wetland_96 | Onshore_18 | Onshore_12 | Onshore_08 | Onshore_01 | Onshore_96 | EditAuthor | EditDate | LCDB_UID | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122856 | Manuka and/or Kanuka | Manuka and/or Kanuka | Manuka and/or Kanuka | Manuka and/or Kanuka | Manuka and/or Kanuka | 52 | 52 | 52 | 52 | 52 | ... | no | yes | yes | yes | yes | yes | Terralink | 2004-06-30T00:00:00 | lcdb1000090321 | MULTIPOLYGON (((1764675.184 5789790.017, 17646... |
| 64325 | Broadleaved Indigenous Hardwoods | Broadleaved Indigenous Hardwoods | Broadleaved Indigenous Hardwoods | Broadleaved Indigenous Hardwoods | Broadleaved Indigenous Hardwoods | 54 | 54 | 54 | 54 | 54 | ... | no | yes | yes | yes | yes | yes | Terralink | 2004-06-30T00:00:00 | lcdb2000139449 | MULTIPOLYGON (((1496215.025 5264808.673, 14963... |
| 491051 | Lake or Pond | Lake or Pond | Lake or Pond | Lake or Pond | Lake or Pond | 20 | 20 | 20 | 20 | 20 | ... | no | yes | yes | yes | yes | yes | Landcare Research | 2004-06-30T00:00:00 | lcdb2000044898 | MULTIPOLYGON (((1209708.330 4902000.847, 12096... |
| 477442 | Exotic Forest | Exotic Forest | Exotic Forest | Exotic Forest | Exotic Forest | 71 | 71 | 71 | 71 | 71 | ... | no | yes | yes | yes | yes | yes | Landcare Research | 2011-06-30T00:00:00 | lcdb2000210008 | MULTIPOLYGON (((1422892.426 5039258.375, 14228... |
| 27336 | Matagouri or Grey Scrub | Matagouri or Grey Scrub | Matagouri or Grey Scrub | Matagouri or Grey Scrub | Matagouri or Grey Scrub | 58 | 58 | 58 | 58 | 58 | ... | no | yes | yes | yes | yes | yes | Landcare Research | 2011-06-30T00:00:00 | lcdb2000163359 | MULTIPOLYGON (((1314716.722 4950010.195, 13146... |
5 rows × 24 columns
%%time
df = pd.clip(df, AKL)
CPU times: user 46.6 s, sys: 10.2 ms, total: 46.6 s Wall time: 46.6 s
df.sample(5)
| Name_2018 | Name_2012 | Name_2008 | Name_2001 | Name_1996 | Class_2018 | Class_2012 | Class_2008 | Class_2001 | Class_1996 | ... | Wetland_96 | Onshore_18 | Onshore_12 | Onshore_08 | Onshore_01 | Onshore_96 | EditAuthor | EditDate | LCDB_UID | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 391151 | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | 69 | 69 | 69 | 69 | 69 | ... | no | yes | yes | yes | yes | yes | Terralink | 2004-06-30T00:00:00 | lcdb1000163624 | POLYGON ((1750839.814 5875665.073, 1750863.262... |
| 377272 | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | 69 | 69 | 69 | 69 | 69 | ... | no | yes | yes | yes | yes | yes | Terralink | 2004-06-30T00:00:00 | lcdb1000168029 | POLYGON ((1746126.136 5990128.894, 1746087.278... |
| 458974 | Herbaceous Freshwater Vegetation | Herbaceous Freshwater Vegetation | Herbaceous Freshwater Vegetation | Herbaceous Freshwater Vegetation | Herbaceous Freshwater Vegetation | 45 | 45 | 45 | 45 | 45 | ... | yes | yes | yes | yes | yes | yes | Terralink | 2004-06-30T00:00:00 | lcdb1000064420 | POLYGON ((1722978.298 5972982.261, 1722971.518... |
| 159353 | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | Indigenous Forest | 69 | 69 | 69 | 69 | 69 | ... | no | yes | yes | yes | yes | yes | Landcare Research | 2004-06-30T00:00:00 | lcdb1000166872 | POLYGON ((1746450.557 5966033.058, 1746439.160... |
| 17374 | Mangrove | Mangrove | Mangrove | Mangrove | Mangrove | 70 | 70 | 70 | 70 | 70 | ... | yes | yes | yes | yes | yes | yes | Landcare Research | 2014-06-30T00:00:00 | lcdb1000424037 | POLYGON ((1751011.045 5917221.658, 1750977.402... |
5 rows × 24 columns
df.Name_2018.value_counts()
Exotic Forest 3981 Indigenous Forest 3673 Manuka and/or Kanuka 2282 Broadleaved Indigenous Hardwoods 1788 Built-up Area (settlement) 1350 High Producing Exotic Grassland 1326 Mangrove 1151 Urban Parkland/Open Space 1099 Estuarine Open Water 441 Orchard, Vineyard or Other Perennial Crop 436 Short-rotation Cropland 362 Lake or Pond 326 Herbaceous Saline Vegetation 303 Low Producing Grassland 291 Gorse and/or Broom 287 Forest - Harvested 266 Sand or Gravel 252 Deciduous Hardwoods 201 Surface Mine or Dump 132 Mixed Exotic Shrubland 120 Herbaceous Freshwater Vegetation 118 Transport Infrastructure 107 River 15 Gravel or Rock 9 Flaxland 9 Fernland 2 Matagouri or Grey Scrub 1 Name: Name_2018, dtype: int64
These classes are far too detailed - simplify to just Urban, Vegetation, Water, Other
def simplify_classes(code):
if code in [1, 2, 5]:
return 1, "Urban"
elif code in [68,69,71]:
return 2, "Vegetation"
elif code in [0,20,21,22,45,46]:
return 3, "Water"
else:
return 4, "Other"
summary = []
years = [1996, 2001, 2008, 2012, 2018]
for year in years:
print(year)
class_year = f"Class_{year}"
df[class_year + "_simplified_code"] = df[class_year].apply(lambda c: simplify_classes(c)[0])
df[class_year + "_simplified_name"] = df[class_year].apply(lambda c: simplify_classes(c)[1])
summary.append(df[class_year + "_simplified_name"].value_counts())
1996 2001 2008 2012 2018
pd.GeoDataFrame(summary).plot.area()
<AxesSubplot:>
%%capture
# %%capture suppresses output
ims = []
years = [1996, 2001, 2008, 2012, 2018]
for year in years:
ax = df.plot(column=f'Class_{year}_simplified_name', legend=True)
ax.set_title(year)
ax.figure.tight_layout()
canvas = ax.figure.canvas
canvas.draw() # draw the canvas, cache the renderer
image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
image = image.reshape(canvas.get_width_height()[::-1] + (3,))
ims.append(image)
imageio.mimsave("land_use.gif", ims, fps=1)
with open('land_use.gif','rb') as file:
display(Image(file.read()))
cols = [f"Class_{year}_simplified_code" for year in years]
cols
['Class_1996_simplified_code', 'Class_2001_simplified_code', 'Class_2008_simplified_code', 'Class_2012_simplified_code', 'Class_2018_simplified_code']
%%time
geocube = make_geocube(
vector_data=df,
output_crs="epsg:2193",
measurements=cols,
resolution=(-100, 100),
fill=0, # NaNs, like offshore areas, will be 0
)
geocube
CPU times: user 20.3 s, sys: 9.94 ms, total: 20.3 s Wall time: 20.3 s
<xarray.Dataset>
Dimensions: (x: 1001, y: 1320)
Coordinates:
* y (y) float64 6.002e+06 6.002e+06 ... 5.87e+06
* x (x) float64 1.704e+06 1.704e+06 ... 1.804e+06
spatial_ref int64 0
Data variables:
Class_1996_simplified_code (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Class_2001_simplified_code (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Class_2008_simplified_code (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Class_2012_simplified_code (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Class_2018_simplified_code (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
grid_mapping: spatial_refarray([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])
array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])
array(0)
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])geocube.Class_2018_simplified_code.plot()
<matplotlib.collections.QuadMesh at 0x7ff56e199a00>
for year in years:
print(year)
outfile = f"output/land_use_{year}.tif"
if not os.path.isfile(outfile):
geocube[f"Class_{year}_simplified_code"].rio.to_raster(outfile, dtype=np.byte) # Use np.byte for smaller output filesize
1996 2001 2008 2012 2018
%%time
pop2013 = pd.read_file("input/statsnzpopulation-by-meshblock-2013-census-FGDB.zip!population-by-meshblock-2013-census.gdb")
CPU times: user 9.29 s, sys: 10.3 ms, total: 9.3 s Wall time: 9.29 s
%%time
pop2013 = pd.clip(pop2013, AKL)
CPU times: user 19.6 s, sys: 0 ns, total: 19.6 s Wall time: 19.6 s
display(pop2013.sample(5))
| Meshblock | MeshblockNumber | Population_Count_Usual_Resident_2013 | Population_Count_Census_Night_2013 | geometry | |
|---|---|---|---|---|---|
| 15957 | MB 0518100 | 0518100 | 192 | 198 | POLYGON ((1753302.613 5915284.124, 1753324.337... |
| 18251 | MB 0705100 | 0705100 | 135 | 135 | POLYGON ((1767892.819 5907131.876, 1767820.019... |
| 15659 | MB 0494806 | 0494806 | 81 | 81 | POLYGON ((1761809.284 5915869.657, 1761809.813... |
| 20092 | MB 0798102 | 0798102 | 102 | 96 | POLYGON ((1773963.418 5896156.722, 1773973.320... |
| 14566 | MB 0389602 | 0389602 | 252 | 249 | POLYGON ((1750597.782 5916672.714, 1750598.204... |
#pop2013.Population_Count_Usual_Resident_2013.replace(0, np.nan, inplace=True)
pop2013.Population_Count_Usual_Resident_2013.plot(kind="hist", bins=200)
<AxesSubplot:ylabel='Frequency'>
%%time
pop2013_cube = make_geocube(
vector_data=pop2013,
measurements=["Population_Count_Usual_Resident_2013"],
like=geocube, # Ensures dimensions match
fill=0 # NaNs, like offshore areas, will be 0
)
pop2013_cube
CPU times: user 2.74 s, sys: 0 ns, total: 2.74 s Wall time: 2.74 s
<xarray.Dataset>
Dimensions: (x: 1001, y: 1320)
Coordinates:
* y (y) float64 6.002e+06 ... 5.87e+06
* x (x) float64 1.704e+06 ... 1.804e+06
spatial_ref int64 0
Data variables:
Population_Count_Usual_Resident_2013 (y, x) float64 0.0 0.0 0.0 ... 0.0 0.0
Attributes:
grid_mapping: spatial_refarray([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])
array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])
array(0)
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])pop2013_cube.Population_Count_Usual_Resident_2013.plot()
outfile = "output/pop2013.tif"
if not os.path.isfile(outfile):
# byte max value is 255, and we have larger values than that here. uint16 max value is 65535, which is fine
pop2013_cube.Population_Count_Usual_Resident_2013.rio.to_raster(outfile, dtype=np.uint16)
%%time
pop2018 = pd.read_file("input/statsnz2018-census-electoral-population-meshblock-2020-FGDB.zip!2018-census-electoral-population-meshblock-2020.gdb")
CPU times: user 10 s, sys: 80 ms, total: 10.1 s Wall time: 10.1 s
%%time
pop2018 = pd.clip(pop2018, AKL)
CPU times: user 21.4 s, sys: 0 ns, total: 21.4 s Wall time: 21.4 s
display(pop2018.sample(5))
| MB2020_V2_00 | General_Electoral_Population | Maori_Electoral_Population | GED2020_V1_00 | GED2020_V1_00_NAME | GED2020_V1_00_NAME_ASCII | MED2020_V1_00 | MED2020_V1_00_NAME | MED2020_V1_00_NAME_ASCII | LAND_AREA_SQ_KM | AREA_SQ_KM | Shape_Length | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19080 | 0767911 | 168 | 18 | 048 | Takanini | Takanini | 3 | Tāmaki Makaurau | Tamaki Makaurau | 0.076304 | 0.076304 | 1842.690356 | POLYGON ((1769823.781 5899194.914, 1769879.451... |
| 914 | 0217601 | 57 | -999 | 018 | Kaipara ki Mahurangi | Kaipara ki Mahurangi | 5 | Te Tai Tokerau | Te Tai Tokerau | 5.475235 | 5.475235 | 12795.361157 | POLYGON ((1736671.915 5938305.093, 1736780.648... |
| 12252 | 0177701 | 120 | -999 | 018 | Kaipara ki Mahurangi | Kaipara ki Mahurangi | 5 | Te Tai Tokerau | Te Tai Tokerau | 4.417407 | 4.417407 | 16595.345385 | POLYGON ((1745943.515 5950593.427, 1746045.393... |
| 18285 | 0713003 | 204 | 21 | 004 | Botany | Botany | 3 | Tāmaki Makaurau | Tamaki Makaurau | 0.031940 | 0.031940 | 938.854375 | POLYGON ((1768777.247 5906637.326, 1768772.926... |
| 48900 | 4006511 | 123 | -999 | 011 | Epsom | Epsom | 3 | Tāmaki Makaurau | Tamaki Makaurau | 0.029792 | 0.029792 | 1084.495229 | POLYGON ((1761006.443 5917698.252, 1760991.042... |
pop2018.General_Electoral_Population.replace(-999, 0, inplace=True)
pop2018.General_Electoral_Population.plot(kind="hist", bins=200)
<AxesSubplot:ylabel='Frequency'>
%%time
pop2018_cube = make_geocube(
vector_data=pop2018,
measurements=["General_Electoral_Population"],
like=geocube, # Ensures dimensions match
fill=0
)
pop2018_cube
CPU times: user 2.53 s, sys: 223 µs, total: 2.53 s Wall time: 2.53 s
<xarray.Dataset>
Dimensions: (x: 1001, y: 1320)
Coordinates:
* y (y) float64 6.002e+06 6.002e+06 ... 5.87e+06
* x (x) float64 1.704e+06 1.704e+06 ... 1.804e+06
spatial_ref int64 0
Data variables:
General_Electoral_Population (y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
grid_mapping: spatial_refarray([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])
array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])
array(0)
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])pop2018_cube.General_Electoral_Population.plot()
outfile = "output/pop2018.tif"
if not os.path.isfile(outfile):
pop2018_cube.General_Electoral_Population.rio.to_raster(outfile, dtype=np.uint16)
%%time
roads = pd.read_file("input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb")
CPU times: user 11.6 s, sys: 20 ms, total: 11.7 s Wall time: 11.7 s
%%time
akl_roads = pd.clip(roads, AKL)
CPU times: user 23.7 s, sys: 0 ns, total: 23.7 s Wall time: 23.7 s
# If a road has a highway number (hway_num not None), it's a highway/motorway
mway = akl_roads[~akl_roads.hway_num.isna()].copy()
mway
| t50_fid | name_ascii | macronated | name | hway_num | rna_sufi | lane_count | way_count | status | surface | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 512 | 100120610 | KAIPARA COAST HIGHWAY | N | KAIPARA COAST HIGHWAY | 16 | 3007739 | 2 | None | None | sealed | LINESTRING (1732000.000 5944172.070, 1732048.5... |
| 2933 | 3198057 | STATE HIGHWAY 1 | N | STATE HIGHWAY 1 | 1 | 3027695 | 2 | None | None | sealed | LINESTRING (1748581.508 5968975.145, 1748558.4... |
| 2934 | 3198059 | STATE HIGHWAY 1 | N | STATE HIGHWAY 1 | 1 | 3027695 | 2 | None | None | sealed | LINESTRING (1748171.047 5971284.152, 1748129.9... |
| 3320 | 3200754 | PAERATA ROAD | N | PAERATA ROAD | 22 | 3000260 | 2 | None | None | sealed | LINESTRING (1767236.112 5888088.508, 1767244.3... |
| 3324 | 3200792 | UPPER HARBOUR MOTORWAY | N | UPPER HARBOUR MOTORWAY | 18 | 3047073 | 4 | None | None | sealed | LINESTRING (1747954.314 5927269.837, 1747970.0... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 138240 | 100048291 | AUCKLAND-WAIWERA MOTORWAY | N | AUCKLAND-WAIWERA MOTORWAY | 1 | 3067966 | 7 | None | None | sealed | LINESTRING (1755881.018 5922863.734, 1755886.4... |
| 138301 | 100048432 | AUCKLAND-HAMILTON MOTORWAY | N | AUCKLAND-HAMILTON MOTORWAY | 1 | 3017109 | 1 | None | None | sealed | LINESTRING (1765115.647 5909916.697, 1765092.7... |
| 138337 | 100048532 | STATE HIGHWAY 1 | N | STATE HIGHWAY 1 | 1 | 3027695 | 4 | None | None | sealed | LINESTRING (1748892.089 5949596.727, 1748892.0... |
| 138369 | 100048589 | PORT ALBERT ROAD | N | PORT ALBERT ROAD | 16 | 3013274 | 2 | None | None | sealed | LINESTRING (1734173.019 5980575.187, 1734175.6... |
| 138680 | 100118365 | SOUTH-WESTERN MOTORWAY | N | SOUTH-WESTERN MOTORWAY | 20 | 3018532 | 4 | None | None | sealed | LINESTRING (1760066.252 5908184.133, 1760043.9... |
426 rows × 11 columns
mway.name.value_counts().head(50).plot(kind="barh").invert_yaxis()
mway.hway_num.value_counts().head(50).plot(kind="barh").invert_yaxis()
ax = mway.to_crs(epsg=3857).plot()
ax.set_title("Auckland Region motorways")
ctx.add_basemap(ax)
%%time
mway_cube = make_geocube(
vector_data=mway,
measurements=["lane_count"],
like=geocube, # Ensures dimensions match
fill=0, # 0 works fine here, as every mway has at least one lane
)
mway_cube
CPU times: user 220 ms, sys: 10 ms, total: 230 ms Wall time: 228 ms
<xarray.Dataset>
Dimensions: (x: 1001, y: 1320)
Coordinates:
* y (y) float64 6.002e+06 6.002e+06 ... 5.871e+06 5.87e+06
* x (x) float64 1.704e+06 1.704e+06 ... 1.804e+06 1.804e+06
spatial_ref int64 0
Data variables:
lane_count (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
Attributes:
grid_mapping: spatial_refarray([6002350., 6002250., 6002150., ..., 5870650., 5870550., 5870450.])
array([1703950., 1704050., 1704150., ..., 1803750., 1803850., 1803950.])
array(0)
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])mway_cube.lane_count.plot()
outfile = "output/mway.tif"
if not os.path.isfile(outfile):
mway_cube.lane_count.rio.to_raster(outfile, dtype=np.byte)
src_ds = gdal.Open("output/mway.tif")
srcband = src_ds.GetRasterBand(1)
dst_filename = "output/mway_dist.tif"
drv = gdal.GetDriverByName('GTiff')
dst_ds = drv.Create( dst_filename,
src_ds.RasterXSize, src_ds.RasterYSize, 1,
gdal.GetDataTypeByName('UInt16'))
dst_ds.SetGeoTransform( src_ds.GetGeoTransform() )
dst_ds.SetProjection( src_ds.GetProjectionRef() )
dstband = dst_ds.GetRasterBand(1)
prox = gdal.ComputeProximity(srcband,dstband,["DISTUNITS=GEO"]) # Encoded value is distance from motorway in meters
# Garbage collection of this variable flushes write
dst_ds = None
dst_ds = gdal.Open(dst_filename)
mway_dist = np.array(dst_ds.GetRasterBand(1).ReadAsArray())
print(mway_dist.shape)
plt.imshow(mway_dist)
plt.title("Distance from motorways in Auckland")
cb = plt.colorbar()
cb.ax.set_title("Distance (m)")
(1320, 1001)
Text(0.5, 1.0, 'Distance (m)')
!ls -Ggh output
total 15M -rw-r--r-- 1 1.3M Apr 15 14:25 land_use_1996.tif -rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2001.tif -rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2008.tif -rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2012.tif -rw-r--r-- 1 1.3M Apr 15 14:25 land_use_2018.tif -rw-r--r-- 1 1.3M Apr 15 14:37 mway.tif -rw-r--r-- 1 2.6M Apr 15 15:16 mway_dist.tif -rw-r--r-- 1 2.6M Apr 15 14:31 pop2013.tif -rw-r--r-- 1 2.6M Apr 15 14:32 pop2018.tif